GGally extends ggplot2 by providing several functions including:
- ggcor(): for pairwise correlation matrix plot
- ggpairs(): for scatterplot plot matrix
- ggsurv(): for survival plot
GGally can be installed from GitHub or CRAN:
# Github if(!require(devtools)) install.packages("devtools") devtools::install_github("ggobi/ggally")
# CRAN install.packages("GGally")
Loading GGally package
ggcorr(): Plot a correlation matrix
The function ggcorr() draws a correlation matrix plot using ggplot2.
The simplified format is:
ggcorr(data, palette = "RdYlGn", name = "rho", label = FALSE, label_color = "black", ...)
- data: a numerical (continuous) data matrix
- palette: a ColorBrewer palette to be used for correlation coefficients. Default value is “RdYlGn”.
- name: a character string used for legend title.
- label: logical value. If TRUE, the correlation coefficients are displayed on the plot.
- label_color: color to be used for the correlation coefficient
The function ggcorr() can be used as follow:
# Prepare some data df <- mtcars[, c(1,3,4,5,6,7)] # Correlation plot ggcorr(df, palette = "RdBu", label = TRUE)
Read also: ggplot2 correlation matrix heatmap
ggpairs(): ggplot2 matrix of plots
The function ggpairs() produces a matrix of scatter plots for visualizing the correlation between variables.
The simplified format is:
ggpairs(data, columns = 1:ncol(data), title = "", axisLabels = "show", columnLabels = colnames(data[, columns]))
- data: data set. Can have both numerical and categorical data.
- columns: columns to be used for the plots. Default is all columns.
- title: title for the graph
- axisLabels: Allowed values are either “show” to display axisLabels, “internal” for labels in the diagonal plots, or “none” for no axis labels
- columnLabels: label names to be displayed. Defaults to names of columns being used.
ggsurv(): Plot survival curve using ggplot2
The function ggsurv() can be used to produces Kaplan-Meier plots using ggplot2 .
The simplified format is:
ggsurv(s, surv.col = "gg.def", plot.cens = TRUE, cens.col = "red", xlab = "Time", ylab = "Survival", main = "")
- s: an object of class survfit
- surv.col: color of the survival estimate. The default value is black for one stratum; default ggplot2 colors for multiple strata. It can be also a vector containing the color names for each stratum.
- plot.cens: logical value. If TRUE, marks the censored observations.
- cens.col: color of the points that mark censored observations.
- xlab, ylab: label of x-axis and y-axis, respectively
- main: the plot main title
We’ll use lung data from the package survival:
require(survival) data(lung, package = "survival") head(lung[, 1:5])
## inst time status age sex ## 1 3 306 2 74 1 ## 2 3 455 2 68 1 ## 3 3 1010 1 56 1 ## 4 5 210 2 57 1 ## 5 1 883 2 60 1 ## 6 12 1022 1 74 1
The data above includes:
- time: Survival time in days
- status: censoring status 1 = censored, 2 = dead
- sex: Male = 1; Female = 2
In the next section we’ll plot the survival curves of male and female.
require("survival") # Fit survival functions surv <- survfit(Surv(time, status) ~ sex, data = lung) # Plot survival curves surv.p <- ggsurv(surv) surv.p
It’s possible to change the legend of the plot as follow:
require(ggplot2) surv.p + guides(linetype = FALSE) + scale_colour_discrete(name = 'Sex', breaks = c(1,2), labels = c('Male', 'Female'))
This analysis has been performed using R software (ver. 3.2.1) and ggplot2 (ver. 1.0.1)
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